OpenRaiser/Intern-Atlas huggingface dataset:
Real-time response estimation of structural vibration with inverse force identification
Venue: arXiv cs.CE 2022
Year: 2022

This study aimed to develop a virtual sensing algorithm of structural vibration for the real-time identification of unmeasured information. First, certain local point vibration responses (such as displacement and acceleration) are measured using physical sensors, and the data sets are extended using a numerical model to cover the unmeasured quantities through the entire spatial domain in the real-time computation process. A modified time integrator is then proposed to synchronize the physical sensors and the numerical model using inverse dynamics. In particular, an efficient inverse force identification method is derived using implicit time integration. The second-order ordinary differential formulation and its projection-based reduced-order modeling is used to avoid two times larger degrees of freedom within the state space form. Then, the Tikhonov regularization noise-filtering algorithm is employed instead of Kalman filtering. The performance of the proposed method is investigated on both numerical and experimental testbeds under sinusoidal and random excitation loading conditions. In the experimental test, the algorithm is implemented on a single-board computer, including inverse force identification and unmeasured response prediction. The results show that the virtual sensing algorithm can accurately identify unmeasured information, forces, and displacements throughout the vibration model in real time in a very limited computing environment.
Smart Agriculture : A Novel Multilevel Approach for Agricultural Risk Assessment over Unstructured Data
Venue: arXiv cs.CL 2022
Year: 2022

Detecting opportunities and threats from massive text data is a challenging task for most. Traditionally, companies would rely mainly on structured data to detect and predict risks, losing a huge amount of information that could be extracted from unstructured text data. Fortunately, artificial intelligence came to remedy this issue by innovating in data extraction and processing techniques, allowing us to understand and make use of Natural Language data and turning it into structures that a machine can process and extract insight from. Uncertainty refers to a state of not knowing what will happen in the future. This paper aims to leverage natural language processing and machine learning techniques to model uncertainties and evaluate the risk level in each uncertainty cluster using massive text data.
Rate-Splitting Enabled Multi-Connectivity in Mixed-Criticality Systems
Venue: arXiv cs.IT 2022
Year: 2022

The enormous quality of service (QoS) demands posed by mission-critical use-cases of future 5G/6G wireless communication raise the need for resource-efficient highly reliable and low latency connectivity solutions. Multi-connectivity is considered a promising yet resource demanding approach to enhance reliability. In this work, we study the potential of the rate-splitting multiple access (RSMA) framework as an efficient way to enable uplink multi-connectivity for data transmissions with particularly high reliability requirements. Mapping high-criticality data onto the common stream allows it to be decoded at multiple access points (APs), which enhances reliability, while the private stream is utilized to serve applications with less stringent requirements. We propose a criticality-aware RSMA-based transmission scheme with short blocklength coding and derive an iterative power allocation algorithm by means of successive convex approximation (SCA). The proposed scheme is shown to achieve an expanded stability rate region compared to two baseline schemes. Moreover, it turns out to be less impacted by short blocklength while leading to substantial rate gains, particularly in the high SNR regime.
Contextually Aware Intelligent Control Agents for Heterogeneous Swarms
Venue: arXiv cs.AI 2022
Year: 2022

An emerging challenge in swarm shepherding research is to design effective and efficient artificial intelligence algorithms that maintain a low-computational ceiling while increasing the swarm's abilities to operate in diverse contexts. We propose a methodology to design a context-aware swarm-control intelligent agent. The intelligent control agent (shepherd) first uses swarm metrics to recognise the type of swarm it interacts with to then select a suitable parameterisation from its behavioural library for that particular swarm type. The design principle of our methodology is to increase the situation awareness (i.e. information contents) of the control agent without sacrificing the low-computational cost necessary for efficient swarm control. We demonstrate successful shepherding in both homogeneous and heterogeneous swarms.
Big Earth Data and Machine Learning for Sustainable and Resilient Agriculture
Venue: arXiv cs.LG 2022
Year: 2022

Big streams of Earth images from satellites or other platforms (e.g., drones and mobile phones) are becoming increasingly available at low or no cost and with enhanced spatial and temporal resolution. This thesis recognizes the unprecedented opportunities offered by the high quality and open access Earth observation data of our times and introduces novel machine learning and big data methods to properly exploit them towards developing applications for sustainable and resilient agriculture. The thesis addresses three distinct thematic areas, i.e., the monitoring of the Common Agricultural Policy (CAP), the monitoring of food security and applications for smart and resilient agriculture. The methodological innovations of the developments related to the three thematic areas address the following issues: i) the processing of big Earth Observation (EO) data, ii) the scarcity of annotated data for machine learning model training and iii) the gap between machine learning outputs and actionable advice. This thesis demonstrated how big data technologies such as data cubes, distributed learning, linked open data and semantic enrichment can be used to exploit the data deluge and extract knowledge to address real user needs. Furthermore, this thesis argues for the importance of semi-supervised and unsupervised machine learning models that circumvent the ever-present challenge of scarce annotations and thus allow for model generalization in space and time. Specifically, it is shown how merely few ground truth data are needed to generate high quality crop type maps and crop phenology estimations. Finally, this thesis argues there is considerable distance in value between model inferences and decision making in real-world scenarios and thereby showcases the power of causal and interpretable machine learning in bridging this gap.
Efficient Exploration using Model-Based Quality-Diversity with Gradients
Venue: arXiv cs.NE 2022
Year: 2022

Exploration is a key challenge in Reinforcement Learning, especially in long-horizon, deceptive and sparse-reward environments. For such applications, population-based approaches have proven effective. Methods such as Quality-Diversity deals with this by encouraging novel solutions and producing a diversity of behaviours. However, these methods are driven by either undirected sampling (i.e. mutations) or use approximated gradients (i.e. Evolution Strategies) in the parameter space, which makes them highly sample-inefficient. In this paper, we propose a model-based Quality-Diversity approach. It extends existing QD methods to use gradients for efficient exploitation and leverage perturbations in imagination for efficient exploration. Our approach optimizes all members of a population simultaneously to maintain both performance and diversity efficiently by leveraging the effectiveness of QD algorithms as good data generators to train deep models. We demonstrate that it maintains the divergent search capabilities of population-based approaches on tasks with deceptive rewards while significantly improving their sample efficiency and quality of solutions.
Improved Multi-GPU parallelization of a Lagrangian Transport Model
Venue: arXiv cs.DC 2022
Year: 2022

This report highlights our work on improving GPU parallelization by supporting compute nodes with multiple GPUs. However, since the default support for multi-GPUs in OpenACC is limited[6], the current implementation allows each MPI process to access only a single GPU. Thus, the only way to take full advantage of multi-GPU nodes in the current version is to launch multiple processes, which increases resource contention. We investigated the benefits of having only one process offload to all available GPU devices.
Projection-free Adaptive Regret with Membership Oracles
Venue: arXiv cs.LG 2022
Year: 2022

In the framework of online convex optimization, most iterative algorithms require the computation of projections onto convex sets, which can be computationally expensive. To tackle this problem HK12 proposed the study of projection-free methods that replace projections with less expensive computations. The most common approach is based on the Frank-Wolfe method, that uses linear optimization computation in lieu of projections. Recent work by GK22 gave sublinear adaptive regret guarantees with projection free algorithms based on the Frank Wolfe approach. In this work we give projection-free algorithms that are based on a different technique, inspired by Mhammedi22, that replaces projections by set-membership computations. We propose a simple lazy gradient-based algorithm with a Minkowski regularization that attains near-optimal adaptive regret bounds. For general convex loss functions we improve previous adaptive regret bounds from $O(T^{3/4})$ to $O(\sqrt{T})$, and further to tight interval dependent bound $\tilde{O}(\sqrt{I})$ where $I$ denotes the interval length. For strongly convex functions we obtain the first poly-logarithmic adaptive regret bounds using a projection-free algorithm.
Completing point cloud from few points by Wasserstein GAN and Transformers
Venue: arXiv cs.CV 2022
Year: 2022

In many vision and robotics applications, it is common that the captured objects are represented by very few points. Most of the existing completion methods are designed for partial point clouds with many points, and they perform poorly or even fail completely in the case of few points. However, due to the lack of detail information, completing objects from few points faces a huge challenge. Inspired by the successful applications of GAN and Transformers in the image-based vision task, we introduce GAN and Transformer techniques to address the above problem. Firstly, the end-to-end encoder-decoder network with Transformers and the Wasserstein GAN with Transformer are pre-trained, and then the overall network is fine-tuned. Experimental results on the ShapeNet dataset show that our method can not only improve the completion performance for many input points, but also keep stable for few input points. Our source code is available at https://github.com/WxfQjh/Stability-point-recovery.git.
Dynamic Appearance: A Video Representation for Action Recognition with Joint Training
Venue: arXiv cs.CV 2022
Year: 2022

Static appearance of video may impede the ability of a deep neural network to learn motion-relevant features in video action recognition. In this paper, we introduce a new concept, Dynamic Appearance (DA), summarizing the appearance information relating to movement in a video while filtering out the static information considered unrelated to motion. We consider distilling the dynamic appearance from raw video data as a means of efficient video understanding. To this end, we propose the Pixel-Wise Temporal Projection (PWTP), which projects the static appearance of a video into a subspace within its original vector space, while the dynamic appearance is encoded in the projection residual describing a special motion pattern. Moreover, we integrate the PWTP module with a CNN or Transformer into an end-to-end training framework, which is optimized by utilizing multi-objective optimization algorithms. We provide extensive experimental results on four action recognition benchmarks: Kinetics400, Something-Something V1, UCF101 and HMDB51.
Breaking the Representation Bottleneck of Chinese Characters: Neural Machine Translation with Stroke Sequence Modeling
Venue: arXiv cs.CL 2022
Year: 2022

Existing research generally treats Chinese character as a minimum unit for representation. However, such Chinese character representation will suffer two bottlenecks: 1) Learning bottleneck, the learning cannot benefit from its rich internal features (e.g., radicals and strokes); and 2) Parameter bottleneck, each individual character has to be represented by a unique vector. In this paper, we introduce a novel representation method for Chinese characters to break the bottlenecks, namely StrokeNet, which represents a Chinese character by a Latinized stroke sequence (e.g., "ao1 (concave)" to "ajaie" and "tu1 (convex)" to "aeaqe"). Specifically, StrokeNet maps each stroke to a specific Latin character, thus allowing similar Chinese characters to have similar Latin representations. With the introduction of StrokeNet to neural machine translation (NMT), many powerful but not applicable techniques to non-Latin languages (e.g., shared subword vocabulary learning and ciphertext-based data augmentation) can now be perfectly implemented. Experiments on the widely-used NIST Chinese-English, WMT17 Chinese-English and IWSLT17 Japanese-English NMT tasks show that StrokeNet can provide a significant performance boost over the strong baselines with fewer model parameters, achieving 26.5 BLEU on the WMT17 Chinese-English task which is better than any previously reported results without using monolingual data. Code and scripts are freely available at https://github.com/zjwang21/StrokeNet.
End-to-End DNN Inference on a Massively Parallel Analog In Memory Computing Architecture
Venue: arXiv cs.DC 2022
Year: 2022

The demand for computation resources and energy efficiency of Convolutional Neural Networks (CNN) applications requires a new paradigm to overcome the "Memory Wall". Analog In-Memory Computing (AIMC) is a promising paradigm since it performs matrix-vector multiplications, the critical kernel of many ML applications, in-place in the analog domain within memory arrays structured as crossbars of memory cells. However, several factors limit the full exploitation of this technology, including the physical fabrication of the crossbar devices, which constrain the memory capacity of a single array. Multi-AIMC architectures have been proposed to overcome this limitation, but they have been demonstrated only for tiny and custom CNNs or performing some layers off-chip. In this work, we present the full inference of an end-to-end ResNet-18 DNN on a 512-cluster heterogeneous architecture coupling a mix of AIMC cores and digital RISC-V cores, achieving up to 20.2 TOPS. Moreover, we analyze the mapping of the network on the available non-volatile cells, compare it with state-of-the-art models, and derive guidelines for next-generation many-core architectures based on AIMC devices.
An information security monitoring and management system for 5G and 6G Networks based on SDN/NFV
Venue: arXiv cs.NI 2022
Year: 2022

An approach to using the concept of Software-Defined Networking and Network Functions Virtualization (SDN/NFV) for the implementation of an information security monitoring and management system in 5G and 6G networks is proposed. SDN switches based on the OpenFlow protocol are offered as network sensors. In order to reduce the time for finding a subset of the right rules in the vast array of all rules on traffic filtering systems that are logically located on sensors, a method of processing and filtering traffic in 5G and 6G transport networks is proposed. This method is based on DPDK with the LPM algorithm and is capable of processing up to 8 megapackets per second on 1 CPU core; the packet processing takes O(1), which is significantly lower than with similar algorithms. The managing subsystem consists of regional monitoring centres and a main one. The main Monitoring Centre includes a main cluster of SDN controllers along with Active/Active redundancy scheme. The regional centres represent SDN software controllers that manage locally subordinate sensors. All the managing centres are interconnected via the Transport subsystem and form a network. An algorithm for network sensor load balancing between SDN controllers has been developed in order to provide fault tolerance, load balancing and network connectivity. The algorithm results in a set of optimal sensor groups with total load not exceeding the maximum capacity of the SDN controllers.
Applying the Web of Things Abstraction to Bluetooth Low Energy Communication
Venue: arXiv cs.NI 2022
Year: 2022

We apply the Web of Things (WoT) communication pattern, i.e., the semantic description of metadata and interaction affordances, to Internet of Things (IoT) devices that rely on non-IP-based protocols, using Bluetooth Low Energy (LE) as an example. The reference implementation of the WoT Scripting API, node-wot, currently supports only IP-based application layer protocols such as HTTP and MQTT. However, a significant number of IoT devices do not communicate over IP, but via other network layer protocols, e.g. L2CAP used by Bluetooth LE. To leverage the WoT abstraction in Bluetooth Low Energy communication, we specified two ontologies to describe the capabilities of Bluetooth LE devices and transmitted binary data, considered the different interaction possibilities with the Linux Bluetooth stack BlueZ, and due to better documentation, used the D-Bus API to implement Bluetooth LE bindings in JavaScript. Finally, we evaluated the latencies of the bindings in comparison to the BlueZ tool bluetoothctl, showing that the Bluetooth LE bindings are on average about 16 percent slower than the comparison program during connection establishment and about 6 percent slower when disconnecting, but have almost the same performance during reading (about 3 percent slower).
A Secure and Intelligent Data Sharing Scheme for UAV-Assisted Disaster Rescue
Venue: arXiv cs.MA 2022
Year: 2022

Unmanned aerial vehicles (UAVs) have the potential to establish flexible and reliable emergency networks in disaster sites when terrestrial communication infrastructures go down. Nevertheless, potential security threats may occur on UAVs during data transmissions due to the untrusted environment and open-access UAV networks. Moreover, UAVs typically have limited battery and computation capacity, making them unaffordable for heavy security provisioning operations when performing complicated rescue tasks. In this paper, we develop RescueChain, a secure and efficient information sharing scheme for UAV-assisted disaster rescue. Specifically, we first implement a lightweight blockchain-based framework to safeguard data sharing under disasters and immutably trace misbehaving entities. A reputation-based consensus protocol is devised to adapt the weakly connected environment with improved consensus efficiency and promoted UAVs' honest behaviors. Furthermore, we introduce a novel vehicular fog computing (VFC)-based off-chain mechanism by leveraging ground vehicles as moving fog nodes to offload UAVs' heavy data processing and storage tasks. To offload computational tasks from the UAVs to ground vehicles having idle computing resources, an optimal allocation strategy is developed by choosing payoffs that achieve equilibrium in a Stackelberg game formulation of the allocation problem. For lack of sufficient knowledge on network model parameters and users' private cost parameters in practical environment, we also design a two-tier deep reinforcement learning-based algorithm to seek the optimal payment and resource strategies of UAVs and vehicles with improved learning efficiency. Simulation results show that RescueChain can effectively accelerate consensus process, improve offloading efficiency, reduce energy consumption, and enhance user payoffs.
Converting OpenStreetMap Data to Road Networks for Downstream Applications
Venue: arXiv cs.DB 2022
Year: 2022

We study how to convert OpenStreetMap data to road networks for downstream applications. OpenStreetMap data has different formats. Extensible Markup Language (XML) is one of them. OSM data consist of nodes, ways, and relations. We process OSM XML data to extract the information of nodes and ways to obtain the map of streets of the Memphis area. We can use this map for different downstream applications.
Data-Codata Symmetry and its Interaction with Evaluation Order
Venue: arXiv cs.PL 2022
Year: 2022

Data types and codata types are, as the names suggest, often seen as duals of each other. However, most programming languages do not support both of them in their full generality, or if they do, they are still seen as distinct constructs with separately defined type-checking, compilation, etc. Rendel et al. were the first to propose variants of two standard program transformations, de- and refunctionalization, as a test to gauge and improve the symmetry between data and codata types. However, in previous works, codata and data were still seen as separately defined language constructs, with de- and refunctionalization being defined as similar but separate algorithms. These works also glossed over interactions between the aforementioned transformations and evaluation order, which leads to a loss of desirable $η$ expansion equalities. We argue that the failure of complete symmetry is due to the inherent asymmetry of natural deduction as the logical foundation of the language design. Natural deduction is asymmetric in that its focus is on producers (proofs) of types, whereas consumers (contexts, continuations, refutations) have a second-class status. Inspired by existing sequent-calculus-based language designs, we present the first language design that is fully symmetric in that the issues of polarity (data type vs codata types) and evaluation order (call-by-value vs call-by-name) are untangled and become independent attributes of a single form of type declaration. Both attributes, polarity and evaluation order, can be changed independently by one algorithm each. In particular, defunctionalization and refunctionalization are now one algorithm. Evaluation order can be defined and changed individually for each type, independently from polarity. By allowing only certain combinations of evaluation order and polarity, the aforementioned $η$ laws can be restored.
IDEALI: intuitively localising connected devices in order to support autonomy
Venue: arXiv cs.HC 2022
Year: 2022

The ability to localise a smart device is very useful to visually or cognitively impaired people. Localisation-capable technologies are becoming more readily available as off-the-shelf components. In this paper, we highlight the need for such a service in the field of health and autonomy, especially for disabled people. We introduce a model for Semantic Position Description (SPD) (e.g. "The pill organiser in on the kitchen table") as well as various algorithms that transform raw distance estimations to SPD related to proximity, alignment and room identification. Two of these algorithms are evaluated using the LocURa4IoT testbed. The results are compared to the output of a pre-experiment involving ten human participants in the Maison Intelligente de Blagnac. The two studies indicate that both approaches converge up to 90% of the time. .